With the large and carefully annotated abdominal multi-organ dataset, we can conduct many basic research topics on medical image segmentation/detection methods and clinical applications. Besides, there are some challenges in the WORD dataset, including the imbalanced sample among large and small organs, the high resolution, the complex anatomical structure. It can be used to develop or evaluate clinical applications, as it is very desirable to develop a tool or software to assist oncologists to delineate organs quickly and accurately. The WORD dataset also can be employed in general algorithms research, such as fully-/semi-/weakly-supervised learning, domain adaption/generalization, partially label and lifelong learning, etc. Here, we roughly summarized the potential research topics as follows: (1) Fully supervised learning [1]} aims to efficiently utilize the labeled data to achieve good results and solve the imbalanced distributions and complex structures challenges. It is a very fundamental topic and has been studied for many years, we present a new dataset WORD to boost this task investigating and evaluating. (2) Annotation-efficient learning [2]}, [3]}, which aims to reduce the label costs and improve the network generalization ability by semi-/weakly-supervised learning, domain adaptation strategies, etc. Recently, they are very hot topics and also show the potential to reduce the annotation costs by utilizing the unlabeled data or sparse annotations. (3) Inference-efficient learning [4]}, which goal is to achieve the trade-off between the high performance and low inference costs. For 3D abdominal CT scans, due to the high dimension and resolution, the inference stage always takes much time and GPU memory. However, there are very few works that have been studied to accelerate the inference of 3D medical image segmentation tasks [5]}. Recently, knowledge distillation has achieved success in several 2D natural image tasks inspiring us that knowledge distillation may have the potential to handle the 3D medical image segmentation tasks [6]}. In this work, we pay more attention to evaluate the existing fully supervised methods on the WORD and introduce a novel weakly supervised method to reduce the label costs.
